Cem May 2020

cem@lists.gking.harvard.edu

4 participants
5 discussions

by Blom, drs. M. - WODC/CRS

Hi All, I can't get why the analyzes below, performed in Stata / R, do not produce the same result. Please provide your suggestions for the explanation for these differences. Thanks, Martine PS (how) can I display the actual cutpoints in Stata (imb)? -- STATA . import delimited "G:\Recidive\Projecten\Verkeer\Algemeen\Artikel effectiviteit ASP tijdens\Data\Data voor R.csv", clear (8 vars, 8,627 obs) . imb d_sekse lftbegrec d_addinfo_ind_best lftinsz1 vgalgexuz vgverkexuz vgrijoiuz, treatment (treat) (using the scott break method for L1 distance) Multivariate L1 distance: .68243298 Univariate imbalance: L1 mean min 25% 50% 75% max d_sekse .01557 -.01557 0 0 0 0 0 lftbegrec .0687 -1.5626 0 -1 -2 -1 -5 d_addinfo_ind_best .00497 -.00497 0 0 0 0 0 lftinsz1 .08852 .91045 0 1 1 2 -12 vgalgexuz .14912 -1.2611 0 0 -1 -1 78 vgverkexuz .09852 -.41727 0 0 0 -1 4 vgrijoiuz .11859 -.36406 0 0 0 0 -4 --R > data <- read.csv(file = "G:\\Recidive\\Projecten\\Verkeer\\Algemeen\\Artikel effectiviteit ASP tijdens\\Data\\Data voor R.csv") > cov <- c("D_SEKSE", "LFTBEGREC", "D_ADDINFO_IND_BEST","LFTINSZ1", "VGALGEXUZ", "VGVERKEXUZ", "VGRIJOIUZ") > imb <- imbalance(group = data$TREAT, data = data[cov]) > imb Multivariate Imbalance Measure: L1=0.665 Percentage of local common support: LCS=11.2% Univariate Imbalance Measures: statistic type L1 min 25% 50% 75% max D_SEKSE -0.015571508 (diff) 0.015571508 0 0 0 0 0 LFTBEGREC -1.562561051 (diff) 0.025019213 0 -1 -2 -1 -5 D_ADDINFO_IND_BEST -0.004970458 (diff) 0.004970458 0 0 0 0 0 LFTINSZ1 0.910454843 (diff) 0.045867098 0 1 1 2 -12 VGALGEXUZ -1.261056065 (diff) 0.146272471 0 0 -1 -1 78 VGVERKEXUZ -0.417271627 (diff) 0.097957912 0 0 0 -1 4 VGRIJOIUZ -0.364063976 (diff) 0.118028583 0 0 0 0 -4 ________________________________ Dit bericht kan informatie bevatten die niet voor u is bestemd. Indien u niet de geadresseerde bent of dit bericht abusievelijk aan u is toegezonden, wordt u verzocht dat aan de afzender te melden en het bericht te verwijderen. De Staat aanvaardt geen aansprakelijkheid voor schade, van welke aard ook, die verband houdt met risico's verbonden aan het elektronisch verzenden van berichten. Ministerie van Justitie en Veiligheid This message may contain information that is not intended for you. If you are not the addressee or if this message was sent to you by mistake, you are requested to inform the sender and delete the message. The State accepts no liability for damage of any kind resulting from the risks inherent in the electronic transmission of messages. Ministry of Justice and Security

3 years, 9 months

Automating progressive coarsening

by Cephas N Ablakwa

Hi Stefano, I sent you a couple of emails last week to your iacus account. Did you get them? Was I suppose to send them to this address instead?

3 years, 11 months

Automating progressive coarsening

by Cephas N Ablakwa

Hi, My name is Cephas Ablakwa, and I am a PH.D. student studying in Program Planning and Evaluation in education. I am working on my dissertation, hoping to defend in June 2020. While using the CEM package in R, I ran into an issue that I hope you can help me with. When automating a progressive coarsening procedure using the relax.cem function, I got the following error: Error1:n : argument of length 0. After googling the issue extensively, I found an answer to a similar error (but with a different function) that suggested there might be a matrix with "one dimension of length 1, without drop=FALSE, R converts a matrix to a vector." The solution offered to this similar problem was lengthy, complex, and specific to the issue. Given my level of experience with R, I do not know how to extrapolate this solution to my issue. In my field and at my university, we mainly use SPSS. I have taken up R mainly to use your CEM package and explore causal inference and coarsened exact matching in my work. I have tried multiple solutions, including restructuring the class of my variables to resemble the class structure of the LeLonde dataset used in the example. Find attached is a summary of the initial steps I took to arrive at the error. I could provide more information if you need it. Any help you can offer will be greatly appreciated. I hope to hear from you soon. Cephas Ablakwa Tennessee Tech University College of Education, PH.D. Candidate |TJ Farr Room 105 cnablakwa21(a)students.tntech.edu [1505503380663_PastedImage]

3 years, 11 months

How to apply CEM to a multi-category treatment

by Alexi Chacon

I am currently trying to use CEM with a multi-category treatment in R. When I try to run a regression between the outcome and the treatments, it does not treat my treatment variable as a multi-level variable. Is it possible to have all treatments in one variable using CEM or does each treatment need to be regressed in a separate model? If it is possible, how is the code written? Best, Alexi

3 years, 11 months

Question regarding indexing of strata options in imbspace package

by Aaron Wells

To whom it may concern: I have successfully been able to execute the imbspace package. I am struggling with identify the coarsenings that apply to a given stratification option. For example, I am interested in knowing the cutpoints used to create #3 below. However, upon running the $coars[[3]] object for this row, the result does not match the $space[3, ] result. I must be missing something obvious as the rows should be indexed identically across objects within a given package. To this end, and apologies for the simple request, but can you help me match the coarsenings to the stratification option. Thank you. Aaron > imbsp_bgnyr$space G0 G1 PercG0 PercG1 Relaxed ML1 var 1 8706 1865 96.29466 91.91720 RTM_MM(3), age(3), PHC_predictive_model_score(3) 0.2554752 PHC_predictive_model_score 2 8596 1867 95.07798 92.01577 RTM_MM(3), PHC_predictive_model_score(3), rtm_rpt_gap_mxrxpmpm(3) 0.2599375 rtm_rpt_gap_mxrxpmpm 3 8614 1866 95.27707 91.96649 RTM_MM(3), PHC_predictive_model_score(3), rtm_pmpm(3) 0.2600584 rtm_pmpm [cid:image002.jpg@01D62C6F.56EB9500] Aaron Wells, PhD Vice President, Outcomes PopHealthCare C: 615-417-5476 E: aaron.wells(a)pophealthcare.com<mailto:aaron.wells@pophealthcare.com> www.pophealthcare.com<http://www.pophealthcare.com/> [C9198E52] IMPORTANT NOTICE: This e-mail message is intended to be received only by persons entitled to receive the confidential information it may contain. E-mail messages to clients of PopHealthCare may contain information that is confidential and legally privileged. Please do not read, copy, forward, or store this message unless you are an intended recipient of it. If you have received this message in error, please forward it to the sender and delete it completely from your computer system.

3 years, 11 months

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Cem May 2020